Download Pupko_pairwise

Homology and sequence
alignment.
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Homology
Homology =
Similarity
between
objects due to
a common
ancestry
Hund = Dog,
Schwein = Pig
Sequence homology
Similarity between sequences as a
result of common ancestry.
VLSPAVKWAKVGAHAAGHG
||| || |||| | ||||
VLSEAVLWAKVEADVAGHG
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Sequence alignment
Alignment: Comparing two
(pairwise) or more (multiple)
sequences. Searching for a series
of identical or similar characters in
the sequences.
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Why align?
VLSPAVKWAKV
||| || ||||
VLSEAVLWAKV
1.To detect if two sequence are
homologous. If so, homology may
indicate similarity in function (and
structure).
2.Required for evolutionary studies (e.g.,
tree reconstruction).
3.To detect conservation (e.g., a tyrosine
that is evolutionary conserved is more
likely to be a phosphorylation site).
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Sequence alignment
If two sequences share a common
ancestor – for example human and
dog hemoglobin, we can represent
their evolutionary relationship
using a tree
VLSPAV-WAKV
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VLSEAVLWAKV
VLSPAV-WAKV
VLSEAVLWAKV
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Perfect match
A perfect match suggests that no change
has occurred from the common ancestor
(although this is not always the case).
VLSPAV-WAKV
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VLSEAVLWAKV
VLSPAV-WAKV
VLSEAVLWAKV
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A substitution
A substitution suggests that at least one
change has occurred since the common
ancestor (although we cannot say in
which lineage it has occurred).
VLSPAV-WAKV
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VLSEAVLWAKV
VLSPAV-WAKV
VLSEAVLWAKV
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Indel
Option 1: The ancestor had L and it was
lost here. In such a case, the event was a
deletion.
VLSEAVLWAKV
VLSPAV-WAKV
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VLSEAVLWAKV
VLSPAV-WAKV
VLSEAVLWAKV
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Indel
Option 2: The ancestor was shorter and the
L was inserted here. In such a case, the
event was an insertion.
L
VLSEAVWAKV
VLSPAV-WAKV
||| || ||||
VLSEAVLWAKV
VLSPAV-WAKV
VLSEAVLWAKV
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Indel
Normally, given two sequences we cannot
tell whether it was an insertion or a
deletion, so we term the event as an indel.
Deletion?
VLSPAV-WAKV
Insertion?
VLSEAVLWAKV
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Global vs. Local
• Global alignment – finds the best
alignment across the entire two
sequences.
ADLGAVFALCDRYFQ
||||
|||| |
ADLGRTQN-CDRYYQ
• Local alignment – finds regions of
similarity in parts of the sequences.
ADLG
||||
ADLG
CDRYFQ
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CDRYYQ
Global
alignment:
forces
alignment in
regions which
differ
Local
alignment will
return only
regions of
good
alignment
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Global alignment
PTK2 protein tyrosine kinase 2 of human
and rhesus monkey
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Proteins are comprised of domains
Human PTK2 :
Domain A
Domain B
Protein tyrosine
kinase domain
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Protein tyrosine kinase domain
In leukocytes, a different gene for tyrosine
kinase is expressed.
Domain A
Domain X
Protein tyrosine
kinase domain
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The sequence similarity is
restricted to a single domain
Domain A
Protein tyrosine
Domain B
PTK2
kinase domain
Domain X
Protein tyrosine
kinase domain
Leukocyte TK
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Global alignment of PTK and LTK
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Local alignment of PTK and LTK
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Conclusions
Use global alignment when the two
sequences share the same overall
sequence arrangement.
Use local alignment to detect regions of
similarity.
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How alignments are computed
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Pairwise alignment
AAGCTGAATTCGAA
AGGCTCATTTCTGA
One possible alignment:
AAGCTGAATT-C-GAA
AGGCT-CATTTCTGA-
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AAGCTGAATT-C-GAA
AGGCT-CATTTCTGA-
This alignment includes:
2 mismatches
4 indels (gap)
10 perfect matches
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Choosing an alignment
for a pair of sequences
Many different alignments are
possible for 2 sequences:
AAGCTGAATTCGAA
AGGCTCATTTCTGA
A-AGCTGAATTC--GAA
AG-GCTCA-TTTCTGA-
AAGCTGAATT-C-GAA
AGGCT-CATTTCTGA-
Which alignment is better?
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Scoring system (naïve)
Perfect match: +1
Mismatch: -2
Indel (gap): -1
AAGCTGAATT-C-GAA
AGGCT-CATTTCTGAScore: = (+1)x10 + (-2)x2 + (-1)x4 = 2
A-AGCTGAATTC--GAA
AG-GCTCA-TTTCTGAScore: = (+1)x9 + (-2)x2 + (-1)x6 = -1
Higher score  Better alignment
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Alignment scoring - scoring of
sequence similarity:
Assumes independence between positions:
each position is considered separately
Scores each position:
• Positive if identical (match)
• Negative if different (mismatch or gap)
Total score = sum of position scores
Can be positive or negative
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Scoring system
•In the example above, the choice of +1
for match,-2 for mismatch, and -1 for gap
is quite arbitrary
•Different scoring systems  different
alignments
•We want a good scoring system…
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DNA scoring matrices
Can take into account biological phenomena
such as:
• Transition-transversion
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Amino-acid scoring matrices
•
Take into account physico-chemical properties
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Scoring gaps (I)
In advanced algorithms, two gaps of one
amino-acid are given a different score than
one gap of two amino acids. This is solved by
giving a penalty to each gap that is opened.
Gap extension penalty < Gap opening penalty
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Homology versus chance
similarity
How to check if the score is significant?
A. Take the two sequences  Compute score.
B. Take one sequence randomly shuffle it ->
find score with the second sequence. Repeat
100,000 times.
If the score in A is at the top 5% of the
scores in B  the similarity is significant.
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How close?
• Rule of thumb:
• Proteins are homologous if they are at
least 25% identical (length >100)
• DNA sequences are homologous if they
are at least 70% identical
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Twilight zone
• < 25% identity in proteins – may be
homologous and may not be….
• (Note that 5% identity will be obtained
completely by chance!)
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Searching a sequence database
Idea: In order to find homologous sequences
to a sequence of interest, one should compute
its pairwise alignment against all known
sequences in a database, and detect the best
scoring significant homologs
The same idea in short: Use your
sequence as a query to find homologous
sequences in a sequence database
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Some terminology
• Query sequence - the sequence with
which we are searching
• Hit – a sequence found in the database,
suspected as homologous
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Query sequence: DNA or protein?
• For coding sequences, we can use the
DNA sequence or the protein sequence to
search for similar sequences.
• Which is preferable?
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Protein is better!
• Selection (and hence conservation) works
(mostly) at the protein level:
CTTTCA =
TTGAGT =
Leu-Ser
Leu-Ser
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Query type
• Nucleotides: a four letter alphabet
• Amino acids: a twenty letter alphabet
• Two random DNA sequences will, on
average, have 25% identity
• Two random protein sequences will,
on average, have 5% identity
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Conclusion
The amino-acid sequence is often
preferable for homology search
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How do we search a database?
• If each pairwise alignment takes 1/10 of a
second, and if the database contains 107
sequences, it will take 106 seconds
= 11.5 days to complete one search.
• 150,000 searches (at least!!) are
performed per day. >82,000,000 sequence
records in GenBank.
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Conclusion
• Using the exact comparison pairwise
alignment algorithm between the query
and all DB entries – too slow
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Heuristic
• Definition: a heuristic is a design to
solve a problem that does not
provide an exact solution (but is not
too bad) but reduces the time
complexity of the exact solution
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BLAST
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BLAST
• BLAST - Basic Local Alignment and
Search Tool
• A heuristic for searching a database for
similar sequences
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DNA or Protein
• All types of searches are possible
Query:
DNA
Protein
Database:
DNA
Protein
blastn – nuc vs. nuc
blastp – prot vs. prot
blastx – translated query vs. protein database
tblastn – protein vs. translated nuc. DB
tblastx – translated query vs. translated database
Translated
databases:
trEMBL
genPept
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BLAST - underlying hypothesis
•
The underlying hypothesis: when two
sequences are similar there are short
ungapped regions of high similarity
between them
• The heuristic:
1. Discard irrelevant sequences
2. Perform exact local alignment only with
the remaining sequences
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How do we discard irrelevant
sequences quickly?
• Divide the database into words of length
w (default: w = 3 for protein and w = 7 for
DNA)
• Save the words in a look-up table that can
be searched quickly
WTDFGYPAILKGGTAC
WTD
TDF
DFG
FGY
GYP
…
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BLAST: discarding sequences
• When the user enters a query sequence, it
is also divided into words
• Search the database for consecutive
neighboring words
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Neighbor words
• neighbor words are defined according to a
scoring matrix (e.g., BLOSUM62 for
proteins) with a certain cutoff level
GFB
GFC (20)
GPC (11)
WAC (5)
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E-value
• The number of times we will theoretically
find an alignment with a score ≥ Y of a
random sequence vs. a random database
Theoretically,
we could trust
any result
with an
E-value ≤ 1
In practice – BLAST uses estimations.
E-values of 10-4 and lower indicate a
significant homology.
E-values between 10-4 and 10-2 should
be checked (similar domains, maybe
non-homologous).
E-values between 10-2 and 1 do not
indicate a good homology
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Web servers for pairwise alignment
BLAST 2 sequences (bl2Seq) at
NCBI
Produces the local alignment of two given
sequences using BLAST (Basic Local
Alignment Search Tool) engine for local
alignment
• Does not use an exact algorithm but a
heuristic
Back to NCBI
BLAST – bl2seq
Bl2Seq - query
blastn –
nucleotide
blastp – protein
Bl2seq results
Bl2seq results
Match
Gaps
Similarity
Dissimilarity
Low
complexity
BLAST – programs
Query:
DNA
Protein
Database:
DNA
Protein
BLAST – Blastp
Blastp - results
Blastp – results (cont’)
Blast scores:
• Bits score – A score for the alignment according
to the number of similarities, identities, etc.
• Expected-score (E-value) –The number of
alignments with the same score one can
“expect” to see by chance when searching a
random database of a particular size. The closer
the e-value is to zero, the greater the confidence
that the hit is really a homolog
Blastp – acquiring sequences
blastp – acquiring sequences
Multiple sequence alignment
VTISCTGSSSNIGAG-NHVKWYQQLPG
VTISCTGTSSNIGS--ITVNWYQQLPG
LRLSCSSSGFIFSS--YAMYWVRQAPG
LSLTCTVSGTSFDD--YYSTWVRQPPG
PEVTCVVVDVSHEDPQVKFNWYVDG-ATLVCLISDFYPGA--VTVAWKADS-AALGCLVKDYFPEP--VTVSWNSG--VSLTCLVKGFYPSD--IAVEWWSNG--
Similar to pairwise alignment BUT n sequences
are aligned instead of just 2
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Multiple sequence alignment
VTISCTGSSSNIGAG-NHVKWYQQLPG
VTISCTGTSSNIGS--ITVNWYQQLPG
LRLSCSSSGFIFSS--YAMYWVRQAPG
LSLTCTVSGTSFDD--YYSTWVRQPPG
PEVTCVVVDVSHEDPQVKFNWYVDG-ATLVCLISDFYPGA--VTVAWKADS-AALGCLVKDYFPEP--VTVSWNSG--VSLTCLVKGFYPSD--IAVEWWSNG--
MSA = Multiple Sequence Alignment
Each row represents an individual sequence
Each column represents the ‘same’ position
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Conserved positions
• Columns in which all the sequences
contain the same amino acids or
nucleotides
• Important for the function or structure
VTISCTGSSSNIGAG-NHVKWYQQLPG
VTISCTGSSSNIGS--ITVNWYQQLPG
LRLSCTGSGFIFSS--YAMYWYQQAPG
LSLTCTGSGTSFDD-QYYSTWYQQPPG
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Consensus sequence
A consensus sequence holds the most
frequent character of the alignment at each
column
A T C T TGT
AAC T TGT
AAC T T CT
AAC T TGT
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Profile = PSSM = Position Specific
Score Matrix
A T C T TG
AAC T TG
AAC T T C
1
2
3
4
5
6
A
1
.67 0
0
0
0
C
0
0
1
0
0
0.33
G
0
0
0
0
0
0.67
T
0
.33 0
1
1
0
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Alignment methods
There is no available optimal solution for
MSA – all methods are heuristics:
• Progressive/hierarchical alignment
(Clustal)
• Iterative alignment (mafft, muscle)
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Progressive alignment
A
B
C
D
E
First step:
Compute the pairwise
alignments for all against
all (6 pairwise alignments).
The similarities are
converted to distances and
stored in a table
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A
B
C
D
A
B
8
C
15 17
D
16 14 10
E
32 31 31
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E
Second step:
A
B
C
D
E
A
Cluster the sequences to create a tree
(guide tree):
B
8
C
15 17
D
16 14 10
The
32 31 31
•represents the order in
whichguide
pairs of treeEis imprecise
sequences are to be aligned
and is NOT the tree which
•similar sequences are neighbors in the
truly describes the
tree
•distant sequences are distant from each
A
evolutionary
relationship
other in the tree
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between the sequences!
B
C
D
E
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Third step:
A
sequence
sequence
B
C
D
E
sequence
sequence
1. Align the most similar (neighboring) pairs
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Third step:
A
sequence
B
C
profile
D
E
2. Align pairs of pairs
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Third step:
A
B
C
D
E
profile
sequence
Main disadvantages:
•Sub-optimal tree topology
•Misalignments resulting from globally
aligning pairs of sequences.
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Iterative alignment
A
B
C
D
E
Pairwise distance
table
Iterate until
the MSA does
not change
(convergence)
Guide tree
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A
B
C
D
E
MSA
Case study:
Using homology searching
• The human kinome
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Kinases and phosphatases
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Multi-tasking enzymes
•
•
•
•
•
•
Signal transduction
Metabolism
Transcription
Cell-cycle
Differentiation
Function of nervous and
immune system
• …
• And more
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How many kinases in the human
genome?
• 1950’s, discovery that reversible
phosphorylation regulates the activity of
glycogen phosphorylase
• 1970’s, advent of cloning and sequencing
produced a speculation that the vertebrate
genome encodes as many as 1,001
kinases
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How many kinases in the human
genome?
• 2001 – human genome sequence …
• As well – databases of Genbank,
Swissprot, and dbEST
• How can we find out how many kinases
are out there?
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The human kinome
•
In 2002, Manning, Whyte, Martinez,
Hunter and Sudarsanam set out to:
1. Search and cross-reference all these
databases for all kinases
2. Characterize all found kinases
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ePKs and aPKs
Eukaryotic protein
kinases (majority)
catalytic domain
Atypical protein
kinases
Sequence homology
of the catalytic
domain; additional
regulatory domains
are non-homologous
No sequence
homology to ePKs;
some aPK subfamilies
have structural
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similarity to ePKs
The search
• Several profiles were built:
based on the catalytic domain of:
(a) 70 known ePKs from yeast, worm, fly, and
human with > 50% identity in the ePK domain
(b) each subfamily of known aPKs
• HMM-profile searches and PSI-BLAST searches
were performed
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The results…
• 478 ePKs
• 40 aPKs
• Total of 518 kinases
in the human genome
(half of the prediction
in the 1970’s)
[1.7% of human genes]
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